A Case Study on the Critical Role of Geometric Regularity in Machine Learning

An important feature of many problem domains in machine learning is their geometry. For example, adjacency relationships, symmetries, and Cartesian coordinates are essential to any complete description of board games, visual recognition, or vehicle control. Yet many approaches to learning ignore such information in their representations, instead inputting flat parameter vectors with no indication of how those parameters are situated geometrically. This paper argues that such geometric information is critical to the ability of any machine learning approach to effectively generalize; even a small shift in the configuration of the task in space from what was experienced in training can go wholly unrecognized unless the algorithm is able to learn the regularities in decision-making across the problem geometry. To demonstrate the importance of learning from geometry, three variants of the same evolutionary learning algorithm (NeuroEvolution of Augmenting Topologies), whose representations ...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Jason Gauci, Kenneth O. Stanley
• Keywords: AAAI 2008, Evolutionary Learning Algorithm, Intelligent Agents, Machine Learning, Machine Learning Approach